The development and implementation of a diffusion model, named FinDiff, which capably synthesizes real-world mixed-type financial tabular data. The utilization of embedding encoding for addressing the challenges inherent to mixed modality financial data, thus fostering efficient data representation.

This work introduces 'FinDiff', a diffusion model designed to generate real-world financial tabular data for a variety of regulatory downstream tasks, for example economic scenario modeling, stress tests, and fraud detection. The model uses embedding encodings to model mixed modality financial data, comprising both categorical and numeric ...

DOI: 10.1145/3604237.3626876 Corpus ID: 261531030; FinDiff: Diffusion Models for Financial Tabular Data Generation @article{Sattarov2023FinDiffDM, title={FinDiff: Diffusion Models for Financial Tabular Data Generation}, author={Timur Sattarov and Marco Schreyer and Damian Borth}, journal={Proceedings of the Fourth ACM International Conference on AI in Finance}, year={2023}, url={https://api ...

8. Generating Synthetic Financial Data. Once the GAN is trained, we can use the generator network to generate synthetic financial data samples. These synthetic data samples will capture the statistical properties of the real data and can be used to augment the original dataset. Let's generate synthetic financial data samples using the trained ...

A GAN, when applied to a data set, learns to generate new data with the same statistics as the training set. This paper examines the characteristics of the time series of financial data developed when a GAN is trained on two sample financial data sets comprising daily S&P 500 Index values, and FTSE 100 index values.

GAN architectures were tested on financial time series, and the generated data was evaluated on its statistical properties, yielding solid results. Finally, it was shown that GANs have made considerable progress in their finance applications and can be a solid additional tool for data scientists in this field.

Synthetic data, artificially generated by computer programs, has become more widely used in the financial domain to mitigate privacy concerns. Variational Autoencoder (VAE) is one of the most popular deep-learning models for generating synthetic data. However, VAE is often considered a "black box" due to its opaqueness. Although some studies have been conducted to provide explanatory ...

In this blog, I briefly introduce our new project: a generative model based on a variational autoencoder (VAE) for quarterly financial time series data. The model simulates a set of eight ...

However, in the financial industry, most data are stored in a tabular format. This paper proposes a sensitivity-based method to assess the impact of inputted tabular data on how VAE synthesizes data.